Improving the Use of Science in Rulemaking

By Daniel R. Pérez
Genetically-modified mosquitoes hold great promise for addressing mosquito-borne diseases that threaten South Florida. Yet, the Florida Keys Mosquito Control District has been waiting since 2011 for approval from FDA to allow the biotechnology company Oxitec to conduct field trials for this promising technology. This public interest comment points out that the trial poses no appreciable risk to human or animal health or the environment. The unusually lengthy timeframe for approval has unnecessarily limited our ability to combat the spread of life-threatening diseases, like Zika and Dengue.

By Susan E. Dudley
Effective environmental policy depends on reliable scientific information and transparent policy choices; it is challenged not only when science is politicized, but also when policy is “scientized.” This paper suggests that current practices scientize policy and threaten not only regulatory outcomes, but the credibility of the scientific process. Using a case study of the procedures by which the Environmental Protection Agency sets National Ambient Air Quality Standards under the Clean Air Act, it illustrates some of the perverse incentives involved in developing regulations, and offers possible mechanisms to improve those incentives and resulting policy.

By Susan E. Dudley & Andrew P. Morriss
This article finds that OSHA's proposed rule would contribute little in the way of new information, particularly since it is largely based on information that is at least a decade old—a significant deficiency, given the rapidly changing conditions observed over the last 45 years. The article concludes with recommendations for alternative approaches that would be more likely to generate information needed to improve worker health outcomes.

By Louis Anthony (Tony) Cox, Jr., Affiliated Scholar
EPA’s quantitative risk estimate (QRA) provides no legitimate reason to believe that the proposed action is “requisite to protect public health” or that reducing the ozone standard further will cause any public health benefits. Given EPA’s information and the unquantified model uncertainty that remains, there is no sound technical basis for asserting with confidence, based on the models and analyses in EPA’s ozone risk assessment, that an ozone standard of 65 ppb would be any more protective than 70 ppb, or that 80 ppb is less protective than 60 ppb. To the contrary, available data suggest that further reductions in ozone levels will make no difference to public health, just as recent past reductions in ozone have had no detectable causal impact on improving public health.

By Randall Lutter et al
Federal and other regulatory agencies often use or claim to use a weight of evidence (WoE) approach in chemical evaluation. Their approaches to the use of WoE, however, differ significantly, rely heavily on subjective professional judgment, and merit improvement. We review uses of WoE approaches in key articles in the peer-reviewed scientific literature, and find significant variations. We find that a hypothesis-based WoE approach, developed by Lorenz Rhomberg et al., can provide a stronger scientific basis for chemical assessment while improving transparency and preserving the appropriate scope of professional judgment. Their approach, while still evolving, relies on the explicit specification of the hypothesized basis for using the information at hand to infer the ability of an agent to cause human health impacts or, more broadly, affect other endpoints of concern. We describe and endorse such a hypothesis-based WoE approach to chemical evaluation.

By Tony Cox
Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims.